{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "78bcb867",
   "metadata": {},
   "source": [
    "# Occupancy Detection data set\n",
    "\n",
    "In this notebook we will prepare and save the Occupancy Detection data Set from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Occupancy+Detection).\n",
    "\n",
    "\n",
    "**Citation:**\n",
    "\n",
    "Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Luis M. Candanedo, Veronique Feldheim. Energy and Buildings. Volume 112, 15 January 2016, Pages 28-39.\n",
    "\n",
    "## Download the data\n",
    "\n",
    "- Navigate to the [data folder](http://archive.ics.uci.edu/ml/datasets/Occupancy+Detection).\n",
    "- Click the **Download** button.\n",
    "- Unzip the file.\n",
    "- Save the file called **datatraining.txt** in the same folder that contains this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "055f12d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c702e859",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-04 17:51:00</td>\n",
       "      <td>23.18</td>\n",
       "      <td>27.2720</td>\n",
       "      <td>426.0</td>\n",
       "      <td>721.25</td>\n",
       "      <td>0.004793</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-02-04 17:51:59</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2675</td>\n",
       "      <td>429.5</td>\n",
       "      <td>714.00</td>\n",
       "      <td>0.004783</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-04 17:53:00</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2450</td>\n",
       "      <td>426.0</td>\n",
       "      <td>713.50</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-02-04 17:54:00</td>\n",
       "      <td>23.15</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>426.0</td>\n",
       "      <td>708.25</td>\n",
       "      <td>0.004772</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-02-04 17:55:00</td>\n",
       "      <td>23.10</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>426.0</td>\n",
       "      <td>704.50</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  Temperature  Humidity  Light     CO2  HumidityRatio  \\\n",
       "1 2015-02-04 17:51:00        23.18   27.2720  426.0  721.25       0.004793   \n",
       "2 2015-02-04 17:51:59        23.15   27.2675  429.5  714.00       0.004783   \n",
       "3 2015-02-04 17:53:00        23.15   27.2450  426.0  713.50       0.004779   \n",
       "4 2015-02-04 17:54:00        23.15   27.2000  426.0  708.25       0.004772   \n",
       "5 2015-02-04 17:55:00        23.10   27.2000  426.0  704.50       0.004757   \n",
       "\n",
       "   Occupancy  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "5          1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "\n",
    "df = pd.read_table(\"datatraining.txt\", sep=\",\", parse_dates=[\"date\"])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eddc2975",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-02-04 18:00:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.175000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.00</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-02-04 18:01:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>690.25</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-02-04 18:02:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>691.00</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-02-04 18:03:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>419.0</td>\n",
       "      <td>683.50</td>\n",
       "      <td>0.004751</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-02-04 18:04:00</td>\n",
       "      <td>23.050</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>687.50</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  Temperature   Humidity  Light     CO2  HumidityRatio  \\\n",
       "10 2015-02-04 18:00:00       23.075  27.175000  419.0  688.00       0.004745   \n",
       "11 2015-02-04 18:01:00       23.075  27.150000  419.0  690.25       0.004741   \n",
       "12 2015-02-04 18:02:00       23.100  27.100000  419.0  691.00       0.004739   \n",
       "13 2015-02-04 18:03:00       23.100  27.166667  419.0  683.50       0.004751   \n",
       "14 2015-02-04 18:04:00       23.050  27.150000  419.0  687.50       0.004734   \n",
       "\n",
       "    Occupancy  \n",
       "10          1  \n",
       "11          1  \n",
       "12          1  \n",
       "13          1  \n",
       "14          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# start data at the hour and finish at the hour\n",
    "\n",
    "df = df[(df[\"date\"] >= \"2015-02-04 18:00:00\") & (df[\"date\"] < \"2015-02-10 09:00:00\")]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec489d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
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       "      <th>humidity_ratio</th>\n",
       "      <th>occupancy</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-02-04 18:00:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.175000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.00</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-02-04 18:01:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>690.25</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-02-04 18:02:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>691.00</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-02-04 18:03:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>419.0</td>\n",
       "      <td>683.50</td>\n",
       "      <td>0.004751</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-02-04 18:04:00</td>\n",
       "      <td>23.050</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>687.50</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  temperature   humidity  light     co2  humidity_ratio  \\\n",
       "10 2015-02-04 18:00:00       23.075  27.175000  419.0  688.00        0.004745   \n",
       "11 2015-02-04 18:01:00       23.075  27.150000  419.0  690.25        0.004741   \n",
       "12 2015-02-04 18:02:00       23.100  27.100000  419.0  691.00        0.004739   \n",
       "13 2015-02-04 18:03:00       23.100  27.166667  419.0  683.50        0.004751   \n",
       "14 2015-02-04 18:04:00       23.050  27.150000  419.0  687.50        0.004734   \n",
       "\n",
       "    occupancy  \n",
       "10          1  \n",
       "11          1  \n",
       "12          1  \n",
       "13          1  \n",
       "14          1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "\n",
    "columns = [\n",
    "    \"date\",\n",
    "    \"temperature\",\n",
    "    \"humidity\",\n",
    "    \"light\",\n",
    "    \"co2\",\n",
    "    \"humidity_ratio\",\n",
    "    \"occupancy\",\n",
    "]\n",
    "\n",
    "df.columns = columns\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21f9a8ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add id\n",
    "ls = []\n",
    "c = 0\n",
    "for i in range(len(df)):\n",
    "    if i % 60 == 0:\n",
    "        c = c + 1\n",
    "    ls.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38effecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>co2</th>\n",
       "      <th>humidity_ratio</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-02-04 18:00:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.175000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.00</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-02-04 18:01:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>690.25</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-02-04 18:02:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>691.00</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-02-04 18:03:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>419.0</td>\n",
       "      <td>683.50</td>\n",
       "      <td>0.004751</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-02-04 18:04:00</td>\n",
       "      <td>23.050</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>687.50</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  temperature   humidity  light     co2  humidity_ratio  \\\n",
       "10 2015-02-04 18:00:00       23.075  27.175000  419.0  688.00        0.004745   \n",
       "11 2015-02-04 18:01:00       23.075  27.150000  419.0  690.25        0.004741   \n",
       "12 2015-02-04 18:02:00       23.100  27.100000  419.0  691.00        0.004739   \n",
       "13 2015-02-04 18:03:00       23.100  27.166667  419.0  683.50        0.004751   \n",
       "14 2015-02-04 18:04:00       23.050  27.150000  419.0  687.50        0.004734   \n",
       "\n",
       "    occupancy  id  \n",
       "10          1   1  \n",
       "11          1   1  \n",
       "12          1   1  \n",
       "13          1   1  \n",
       "14          1   1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"id\"] = ls\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9a23a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
       "       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
       "       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
       "       131, 132, 133, 134, 135], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a693a267",
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-order columns\n",
    "\n",
    "df[[\"id\"] + columns[0:-1]].to_csv(\"occupancy.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9001346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "Name: occupancy, dtype: int32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create target variable\n",
    "\n",
    "occ = (df.groupby(\"id\")[\"occupancy\"].mean() > 0.5).astype(int)\n",
    "\n",
    "occ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1709983",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save target variable\n",
    "\n",
    "occ.to_csv(\"occupancy_target.csv\", index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7c509e",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0345e6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef09250b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>co2</th>\n",
       "      <th>humidity_ratio</th>\n",
       "      <th>occupancy</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-04 18:00:00</th>\n",
       "      <td>23.075</td>\n",
       "      <td>27.175000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.00</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 18:01:00</th>\n",
       "      <td>23.075</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>690.25</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 18:02:00</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>691.00</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 18:03:00</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>419.0</td>\n",
       "      <td>683.50</td>\n",
       "      <td>0.004751</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 18:04:00</th>\n",
       "      <td>23.050</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>687.50</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     temperature   humidity  light     co2  humidity_ratio  \\\n",
       "date                                                                         \n",
       "2015-02-04 18:00:00       23.075  27.175000  419.0  688.00        0.004745   \n",
       "2015-02-04 18:01:00       23.075  27.150000  419.0  690.25        0.004741   \n",
       "2015-02-04 18:02:00       23.100  27.100000  419.0  691.00        0.004739   \n",
       "2015-02-04 18:03:00       23.100  27.166667  419.0  683.50        0.004751   \n",
       "2015-02-04 18:04:00       23.050  27.150000  419.0  687.50        0.004734   \n",
       "\n",
       "                     occupancy  id  \n",
       "date                                \n",
       "2015-02-04 18:00:00          1   1  \n",
       "2015-02-04 18:01:00          1   1  \n",
       "2015-02-04 18:02:00          1   1  \n",
       "2015-02-04 18:03:00          1   1  \n",
       "2015-02-04 18:04:00          1   1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set date as index\n",
    "\n",
    "df.set_index(\"date\", inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "99c291be",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LdzEwFBrzuq4Bb9F7KiNcDdUKuLJFAZeIiEgReHFTFwB/XrJhzOu6+v0RrhQCrhVbuvn3ym3JN07GpYBLRESkwDm3q6B0uVnc63oDQb57z0vArrxayegd9EbPwmEVsM40BVwiIiIF7ku3vRC9PVbJnu/dsyI6QpVKwPW+Y+cBMBAce9pSkqeAS0REpMD99vF10dud/fETk/bGrL9KZdH83tMaALSOKwsUcImIiBSw3RfJP7NuZ9xry8t2TTc21yUfcNVXefUX+wIa4co0BVwiIiIFbEfv4LD7ty/dxPLNXaNeWxYTcDWmMMJVV+VNQ/YOaoQr0xRwiYiIFLCtXQMA/ODcQ6PHPnnzM6NeO9aC+kRE6in2DWqEK9PGDbjM7HozazOzF0Y5999m5sxsqn/fzOynZrbKzJ4zsyNirr3IzFb6/y7K7LchIiJSms75+aOAN11Y50/5hdzouwjL0ou3qKv2nl9ruDIvkRGuXwOn7X7QzOYApwDrYg6fDizw/10CXONfOxn4CnAMcDTwFTNrSafhIiIiE8lR8ycTiadWt/eOek1ZmhFXvT+lqDVcmTduwOWcexjYMcqpHwH/A8SG2WcBNznP40Czmc0ATgXuc87tcM51APcxShAnIiIiw02pr+KCo+cwq7l22PG+UdZZGekFXJERNK3hyryU1nCZ2VnARufc0t1OzQLWx9zf4B+Ld3y0577EzBab2eL29vZUmiciIlISegNBtvcOMmdy3Yhzm3YOjDiW7pRiJDv9Y69sT++JZISkAy4zqwM+D3w5880B59y1zrlFzrlFra2t2fgSIiIiReGmx9YCMK2xBtiVCR521UyMVZ5mxDXJT5b612c2pvU8MlIqI1x7A/OBpWa2BpgNLDGzPYCNwJyYa2f7x+IdFxERkTi+c7dXpufVbT0jzo2WANXS3KVoZjTXVfKmA6an9TwyUtIBl3PueefcNOfcPOfcPLzpwSOcc1uA24H3+rsVXwN0Ouc2A/cAp5hZi79Y/hT/mIiIiIwiGApHb194zJ4jzneNEnCVZyDZ09zJdQzFfG3JjETSQtwMPAbsa2YbzOziMS6/E1gNrAJ+CXwMwDm3A7gSeMr/93X/mIiIiIyio29XQDW9sRqA0w7cI3rs5ifXjXhMWZojXODtVOxNIi1EW9cAb/3Zf2jrGrmmTHYZt7Klc+6Ccc7Pi7ntgEvjXHc9cH2S7RMREZmQYjPMV/hDVz9456GcvXIWH/nt0zy+euS4RbpTigCrt/WwtSuQ8PW/fWIdz23o5PdPruO/3rgw7a9fqpIvJS4iIiJZt3tJH4D66gpOO2gPmusqmdFUO+J8+uEWdPTGL44tqVNpHxERkQK0vdcbZXr7EbNHnNvZNzRqPcXuAW8qMJJPKxXvf908qisUHmSaelRERKQAfeqWZwH43On7JvyYBdMbAPjWOQen/HVrKssJBMOEw6OXD4onTrUh8SngEhERKUCRUarJdVUjzn349XtRXmYjgqKaSu/P+qFzmlP+urX+1w0EE9upmIlpzIlAAZeIiEgBOnbvKew7vTG6YD7W7OZaQmHHtp7hi9sjo0zpBEG1lV7A1T+keoqZpIBLRESkAO3oHaS5rnL0k/5uxP/6w7NjnU5JsgGXZhITo4BLRESkAO3sG2Jy/cjpRIATFkwF4NHdah5mYh1VjT+l2J9kAesMZKQoaQq4REREClB7TyDuCNeeU+oBOHDmpGHHI/GWpTGpuHiNl9/rt4+PTKw6Fi2aH5sCLhERkQKzbnsfO/uGePCl9rjXHDq7iakN1Rn/2pE6ilMbRh9d250GthKjgEtERKTArPaLVY9V07CproqdfcOTozp/mCmd6b1DZjcDXnoIyRwFXCIiIgUmsnD9h+cdFvealrpKdo5SwDpdk2oqKC+zUTPdS+oUcImIiBSYPn+HYGNN/Ap8zbWV7OwbHnD1DXqPS2d0ysxoqauio08BVyYp4BIRESkwL2zoBMYu0eOAzv4h2roHosc2dfZTVV7GlDi7GxM1q6WWZZtGlg6S1CngEhERKTCvtHtruGY2jyxQHbGqzbvmpw+sjB7b0jnA9KZqysrSW8p+0MxJrO/oT+s5ZDgFXCIiIgVmj6ZaqsrLmFQTJ/Ep8IHXzQdgTktd9NjmzgFmNMUP0hI1pd6bUgwlWU9R4lPAJSIiUmD6B4PRmobxHOcnPw3FJMDa3NnPjKaatL/+5PoqnCOpdVwKzcamgEtERKTAbOsdpCVeWR9fZGH8lk5vDZdzjq2dAfbIQMDV5H/trgR2QSrDfGIUcImIiBSYO57bTKKzeXc8txmAnkCQwVCYqfXpJ0NtrPYCru6B5Mr7SHzjBlxmdr2ZtZnZCzHHvmdmL5nZc2b2VzNrjjl3hZmtMrMVZnZqzPHT/GOrzOzyjH8nIiIiJaB7wBtVWrejb9xrG6orouV/IikimsYZGUtEZPQsEIyfeDVCJX0Sk8gI16+B03Y7dh9wkHPuEOBl4AoAMzsAOB840H/Mz82s3MzKgauB04EDgAv8a0VERCRGbyCU8LWv3XsKleXen/JOf/qvuTb9gCtiQ8f4QZ8kZtyAyzn3MLBjt2P3Ouci44yPA7P922cBtzjnAs65V4FVwNH+v1XOudXOuUHgFv9aERERidET8AKndy6aPc6VXnAVCbSiAVddejm4AFa2dQPDU07EozVcicnEGq4PAHf5t2cB62PObfCPxTsuIiIiMSLrpk4/aMa41zbVVrK5c4ChUDg6pdicgSnFtx/pBXvnLpqT9nOJJ62Ay8y+AASB32WmOWBml5jZYjNb3N4ev0q6iIhIKeoJeAFXwxhlfSKmNHgL5Dd29LOz30vh0JSBKcXGaq+eYt+gFs1nSsoBl5m9D3gz8C7nokvmNgKx4fBs/1i84yM45651zi1yzi1qbW1NtXkiIiJFqccf4RqrjmLEwukNgDedGF00n4GAy8xoGqVW45i0en5MKQVcZnYa8D/AW51zsSvqbgfON7NqM5sPLACeBJ4CFpjZfDOrwltYf3t6TRcRESk9P7r/ZQCqysf/Ex0Jrjr7h+jsH6K6oiytwtWxYteHSfoSSQtxM/AYsK+ZbTCzi4GfAY3AfWb2rJn9AsA59yJwK7AMuBu41DkX8hfYfxy4B1gO3OpfKyIiIjFm+fUT50yuG+fK4QFX98BQRka3IsrLjOWbEy9gPaQyQGMad7zSOXfBKIevG+P6bwLfHOX4ncCdSbVORERkgqmtKqeqvCya7mEswwOuYELrvhK10i+OPZ7ItOM1D73C507bL2Nfv9Rk7icjIiIiabvz+S0JXzspJuDqCQRprM78n3XnHDZG7gdlo0+MSvuIiIgUqZrKcqoryujqH+KhFe0s3dCZsee+7E0LAegfSjwRq8SngEsA2N4T4NCv3ctDK9ry3RQRkQkrGPJK6VSWJ55NtClLi9un+iknuvrHHsFyaO1WIhRwCQBHfuN+OvuHeN8NTxEI6tOMiEg+7Oj1cml9+c2JV79rqq1k485+APZurc9YWzr6vLbcv3xrxp5zIlPAJSPct2wrn7rlGZxyqoiI5FRbdwCA1sbqhB/TVFvJv1duA+CV9t6MtWXRni3ArkSskh4FXALAATMmRW9//PfPcNuzm/jXy+Nn+n95azc7/U9BIiKSnm09yQdcsQlS77/shIy1ZZ9pXlLV2vHyeumzeUIUcAnAqPP/97w49jDyYDDMKT96mMO+fl+2miUiMqFs6PCmBiPrpxLREZMNfp9pjRlrS22VF2it2NqdseecyBRwCYFgiI07+znz4OGFUhuqx/5Us/CLd0Vva92XiEj6rn14NZDcCNez63cC8LbDZ2W0LTUV3t+A3z+xbszrNMCVGAVcwvLN3qeX2S21w37J65PI5/K3Z0YtjSkiIklYNM9bN1VXlXw+rQ8cNz+jbSkrS3ynpIxPAZdEpxMPn9tCR++u9VjJFC298h/LGQqF+eTNz3DZrc9muokiIhNCZVkZ0yclProF8L13HALAAr+QdSbVVZUzo6km4887ESnT/AT3jX8s41f/eRWAmc01BGNqYe3ojb8YfmC3RHg9gSALvrBrivGyNy1kVnPtmNmJRURkuEAwlHTx6XMXzeHcRXOy0p6T9pvGi5vGrqeoHe2J0QjXBBcJtsDLWPz6ha3R+x1j7D7c7gdj//XGBaOeP+47D/K3ZzXNKCKSjL89u4m12/vy3YyoxpoKle7JEAVcElVbWc6cybXR+2ONcC33P/HMbK6Ne83//Wt15honIiI511hTSfdA5rPYT0QKuCSqvMw4fI63YHNWc+2Ya7jWbPeS681sih9wvbRFW4lFRJIxqaaCi167Z76bEVVRZgSCYdr9hKySOgVcE9Bdz2/m9qWbhh37xEn7MKOphrcdMYuHPnMiZxy8x5gjXN+4YzkAe2WwjISIyETXNxiiLokd4tl22JxmAJZtjr+OSyu4ElM4P1XJifuWbeWjv1sCwIJpu3a0/Pcp+0Zvz5taz2AwTP9QiKfX7uDIPSfHfb7J9VV88Lj5tPcEuO3ZTRw2pzmaEwZgKBSmslxxvYjIeIZCYYJhN35m9xzadw8vkWpb10CeW1L89JdwAlm5tZsP3bQ4ev8Dv34KgFMPnD7i2iXrdgLw9msei/t8Zx82k5rKcr745gP4yfmH89BnTuSvHzuWg2c1Ra9Z1daTodaLiJS2yO7vQgq4pjV6KSHaxphS1CbFxCjgmiAGhkK86UcPDzu2udP7xPLFM0dWpf/mOQfFfa6wnzpi7pTh04nzptZjZvz9E8dx2ZsWAvDchp3pNFtEZMIYGAoDUFNZOH+aa6vKaayp0BquDCicn6pkVWxphjMPGV7CZ3bLyIXvB85sGnEsos//FFZVHj/H1tmHeSUmysv0EhMRSURkhCvZPFzZNq2xmrZuTSmma9y/hmZ2vZm1mdkLMccmm9l9ZrbS/7/FP25m9lMzW2Vmz5nZETGPuci/fqWZXZSdb0fiCYbD0ds/u+DwYZ+gRktOWj5GSYel/hqtdTvi54pprq8EGJa5XkRE4ovkPmwooEXz4E0rbu0aY0oxh20pZokMP/waOG23Y5cDDzjnFgAP+PcBTgcW+P8uAa4BL0ADvgIcAxwNfCUSpEluPPbKdsAbzTKz6ND1WBXpLzxmLlMbquKeP+fw2XHPNVZXUFFmYyZPFRGRXbb4yzzmTK7Lc0uGmzZJI1yZMG7A5Zx7GNix2+GzgBv92zcCZ8ccv8l5HgeazWwGcCpwn3Nuh3OuA7iPkUGcZNGDK9oBuO6io4Yd/9Kb94/7mA0d/WzrGRxRxqd/0LtfXx1/2NvMCIYdP3/olVSbLCIyofRHFs1XFdaU4raeAOt39NOlBKhpSXWBzXTn3Gb/9hYgss1tFrA+5roN/rF4x0cws0vMbLGZLW5vb0+xeRLPjGZvx8kHXudVlX/zITPjXvvCxk4AlqzrGHa8vwB30oiIFLtC3KUIUOGvxX1i9e5jLx7VUkxM2iuandfTGett59y1zrlFzrlFra2t4z9AErJozxb2aq1nUo23turLbzmANVedOeZarV++90iAEXW0IiNc430K+9xp+wHoU5GISAKi760FFnB99lQvT2O8wErhVmJSDbi2+lOF+P+3+cc3ArEly2f7x+IdlxzpGwwxf0pyWeFnt3jrCHZPeJfoCNceTd76sO09WsclIjKeFVu9vIWFtkux3l/E3zuoItbpSDXguh2I7DS8CLgt5vh7/d2KrwE6/anHe4BTzKzFXyx/in9McqQnEKShJrmdL63+gvov3fbisON9CY5w1VZ6X2+rMhSLiIzr5ie99D2FlIcLdu2afG5D56jn48+TSKxE0kLcDDwG7GtmG8zsYuAq4E1mthJ4o38f4E5gNbAK+CXwMQDn3A7gSuAp/9/X/WOSI90DQzQmGXCVxZlujIxw1VSMHXCF/eHnb925PKmvK4lbsq6Dr97+YjQZrYgUv9FS9eTTlHpvt/oNj6wZ9bzefRIz7l9g59wFcU6dPMq1Drg0zvNcD1yfVOskI5xzdA8EafTXb6VrYChETWVZ3IAs4qT9pgFwxFxlAMkG5xxv+/mjAJx9+KxokVkRKV6HzI6fdDpfxnuvl8QU1rilZMXAkFcQNdkRLoBD/T/ioZgRlL7BIHVV4z9XTWU59VXlYy7Ml9QFgruS2ZYX2CdiEUlOZIdivGm7fDt0TjN7Tims/GDFRgHXBNDt7xJMZYTr+H2mAvDSlq7osf7BcMK7aBpqKugNaKFlNsT261NrNEMvUswiu7nfdsSoGZPybun6nazdHqe6iOYUE6KAawLY4i9abx0ja3w8J+/vTQtGMiDDrinFRGztCnDLU+vHv1CSFtm8APD1fyzLY0tEJF29Ae/3+Tj/Q26hmT81/i53p4grIQq4JoCNHf1AauUiZjR5ha23xOw0THRKUTLn5B88xMd/v2TYsW098WubiUhxiYxY1xdYHcWIfaY1AEpymg4FXBNArz8SMimFKcXWxmrKy2zYCFf/UCjhKcXL3rQQ8II0Sd0r7b3847nNPB+zvmP33Z/9g6HdHyYiRSIScBVa4eqI+5ZtBWD1tt48t6R4KeCaACK/yHUp1OcqLzOmNVazeVjAFaY6wSnFGU1eKaFt3Up+mqrYtVqvtHuJEQeDYZ5aM7zk0prteiMUKVaRpKKFOsL1mVO8D88vbe7Oc0uKlwKuCSDdX+Q9mmqGjXAFhkIJZ0Ke7Odv+ddK1cVMVVv3rqnD//rDsyxdv5M7nt804rqhUHjEMREpDj3+Gq6G6sLKMh9x2kF7AHDbsyoSkyoFXBNAR683ulRdkdqPe/nmLv6zalv0/mAonPBztfgB15f+9kJKX1vgz09vGHb/rKsf4dN/WBq9/8v3LgKGL6IXkeKyayaiMEe45vml4Q6aNTJPmJZ1JUYB1wTw7PqdQOrZi6f6JX7W+VuCA0NhqsfJMh8RSXq67/TGlL62wM8eXDXm+emTvJ+PSiiJFK9CXzRfUe6FCw8s3zriXKG2udAo4JoAmmorqUgj+eiZB88A4JLfLAa8Ea6qJEfLVmztZq3WGGVFJCntp255Nr8NEZGU9UQCrhTW2ubS0lESs552oDfdeOzeU3LdnKKigGsCGAo5Dpw5KeXHRzLUR9JLBIZCKU1P/mWJ5v5T0VhdER3FGs0hs5tz1xgRyYreQJCayrLoSFIxKfObnOqylYlCvTMB9A+GqE3jU9MHj98LIPocgWDiuxQB/vSR1wIws7km5TZMVOGwo2cwyHlHzR31/OdO20+lk0RKQO9gqGBTQkREZjvi5eLSUq6xKeCaAHrTTFQa2ZHY1h3AOecvmk88gNt/hje61tE3lHIbillb9wB7XXFHSuV3ugeCOAeTRqmD+av3LuKjJ+4NwPELvOzUgaAWzosUo95AsODXQkVGsOKW+JExKeCaAPoHQynl4IoVWbM1GArjXHJDx5GvfdVdL6XVhmLjnOOvz2zg/Tc8RdjBhb98POnnWL3Ny7s1Z3Idnz1132Hn3njA9Ojt0w/yPnnu6FW+M5Fi1BsIUl+gOxQj3nLYTAC2x3mf0W7FsSngmgD6MhBwvePI2UxtqGLpem/BZE8SBaljd0cunkBFlj9002I+/YelvLjJK/w9FHJJB0SRHFyzmmu59A37cNMHjgbgNxcfPey6SL6zV9q0MUGkGPUEggU/pTjFf5/p2O19TIFWYhRwTQDpTikCTG+sYXvvID+4dwUAj8bk5UrGqxOoLMT9y9tGHPtDkoW8O/u9adimWq8s0wkLW1lz1Zkcv6B12HU7+7w3wHdf90QqTRWRPOsNhKgv0KSnEY1+ebh4H7hTzDw0YSjgmgDSXTQPsHB6A87BE696I1RnHTYrqcfPm+IVzi70T3CZsn7H6GscvnP3SwSTyAjf7o9wjVcH8+zDvZ/HGQfvkfBzi0jh6A0EqSvw98fI+/dzo6SGAI10jUcBV4kbCoUJhh11CZbiiaepbvgf/Pe8ds+kHn/9+44CYGCCLOr++3MjS+9EfP6vzyf8PN+7xxtRbBxl0Xysmspy5kyuTWozg4gUjp5AkIYCX8MVeR+6/pFX89yS4qSAq8T1D3kBTrojXJEpLfDm8SuTzBUT+fr9gxOj3t+s5tph9+/99AnR28v94q/rd/Qx7/I7OPvqR8Z9vrIEUj8EQ46/PrORcFgfM0WKTd9gqOB3KUZ2rB86e2R5H1BaiPGkFXCZ2afN7EUze8HMbjazGjObb2ZPmNkqM/uDmVX511b791f55+dl5DuQMXX6qRgCwfQCndjdM6kkt6v1f1EjAWAp29o1EM36/tBnTuTxK05m4fRGvn/uoQDMn1qPc47P/smrhxgpvZSuSN8+tnp7Rp5PRHLDOUfvYLBgC1fHOmR2U7RGriQn5YDLzGYBnwQWOecOAsqB84HvAD9yzu0DdAAX+w+5GOjwj//Iv06ybONOLzt8uosZ9/TXYAFUpzA9Gfnk1pvE7sZi9b4bnorenjapmj2avISv7zhyNofMbuL2pZuYf8WdPL567B2boSRHqn5y/uEAXD1O7UURKSx9gyGcK46ahI01FSxe0zHsWGTtVryEqOJJd0qxAqg1swqgDtgMnAT8yT9/I3C2f/ss/z7++ZMt1WrKkrCw/wsQKSKdKjOL5uKqSqH0RGV5GXVV5XT1l37y0+Wbu6K3a3cLTuMtNh3N6nYvB9cJC1vHudJz2JxmAB59RSNcIsWk0AtXxxoYCieVFkh2STngcs5tBL4PrMMLtDqBp4GdzrnIT2MDENnONgtY7z826F8/otKlmV1iZovNbHF7e3uqzRPfjY+uAUg7DxfAoD8tGRk1S9akmspomoNSNRAzZVpRZuz+mWK/PRoTfq7INPCFR89J6PrIOruZTSqhJFJMIgFMMeziPmm/aQAs29Q14pzGUMaWzpRiC96o1XxgJlAPnJZug5xz1zrnFjnnFrW2JvbJXuK758WtALQ2xi9+nKxUP91Mqq2ga6C0A679vnR39Paqb50x4vy333Zw9PYb9m2NZo8frU8ja7KS+dQ7f2o9mzoHonm5RKTw9QaS/13PlxP8HIAbOkamvtGU4tjSmVJ8I/Cqc67dOTcE/AV4HdDsTzECzAY2+rc3AnMA/PNNgOY+sih2t9qMptoxrkzMJ0/aJ63Hb+zojwaAE1Vk2g/ghvcfHS3ovbVrYMS1kU+Qu09LjiWSWPa91z+ZRitFJJd6olOKhb9ofnKDt2D++Y2JL48QTzoB1zrgNWZW56/FOhlYBjwIvMO/5iLgNv/27f59/PP/dAqHs6p7wPsl/vKbD8jI8737NXv6/89N6fG9g6W/Q/EQf7v07z90zKjnzYzV3zqD1f7o1/RGL+Bq6wqMuPYrt78I7NqKnYiLj5sPUPJTtyKlpG/QD7gKPA8X7Crv87//1OacZKWzhusJvMXvS4Dn/ee6FvgccJmZrcJbo3Wd/5DrgCn+8cuAy9NotyRgZ783rRSbQysd0ybVsOaqM/nG2QePf/EoTvGLLZdynqjnNnRy5sEzOHbvqXGvKSuzaF6taZO8qd4f3f9y3OvnxuwQHc8XztgfgMNjRtJEpLD1FNGi+cgHwMh61PbuAKu39eSzSUUjrZ+uc+4rwFd2O7waOHqUaweAc9P5epKclVu9X4JJGQq40rX/jEncu2wrq9p7WDg98cXjxeLlrV5C0zue38zVCT5m2iRvhOvJV3cwMBQadTRrvLI+scrKjP1nTGJ7kkWyRSR/Imu4imHRfMRLW7p5YvV23n3dEwyFSvdDdCYp03wJ+9BvFgOwdnthFIw+Yk8vNcX//Om5PLckOzr8IOfYvUdsvo2rMeYNdvcgaVpjNecfldgOxVjLN3fx75WpFRcXkdzrLaI1XLEeXNGuYCsJCrhKWGSF3JmHzMhvQ3zHzJ8MwEtbRm4nLgV9/q7Cz/g7DxNhZlz7niMB2NEzPODa1hNIav2WiBSn6JRiEazhArjh/V5t3D2TWO4gCrgmhEzsUMyEmspyFk5v4MSF0/LdlKyIpGJoqUuu7MWUBm8d17beXQvnl23qIuzg4ZXJ56KLpJoYmABllERKQW8gSF1VeUI1UwvBIn+2onu3ND/aBje24ginJSWzmms5YOakfDdjmObaKjpKNEdUz0BqyQsju35iR7ge9+shrm5Pfjq4uc5b87Wzb4g9mjRCJlLoegeDRbFgPiIyEvetO1/Kc0uKi0a4SlhPIFhwWceb6ko323xPigtfp/h5bXbErOFa2eZtePjJ+Ycl3Y45Ld4wf6Q0kIgUtt5AqKgWzMcbiXNoiGssCrhKVCjs6BoYoinJ6a1sa6mrLNkRru/c7X3aq6lM7teqobqCmsqyYSWTIotozzg4+fV386fWA7AhxRJMIpJbkSlFKW0KuEpU98AQzkFzgaSEiGiuq2JnX2mOcEUkW0/MzJg7uY4tnbuyzU9pqGJSTQWVKRQKj4yY/f6JdUk/VkRyrydQXFOKAFeefdCIY0ZxrEHLl+L6CUvC1m736lwV2jbjPSbVEAiGae8OZLS+YyHYc0od01L8nlobq2nv2bVoPhAMU1WR2s+uzl9fkWrNSxHJrb7BUNG9H462XEVTimOb8AHXvMvvAGDf6Y3c8+kT8tyazLlvmVezsNCqt0cyq3f0DRbdG8x4hoJh9pxSn9JjWxuqWbJuZ/R+YChMdUXqA9DzptRxwIzC2jAhIqPrGwxSW1VcKRZa6gtruUoxmNBTioHgrm3zK/ws4aWi1l8P8NZDZ+a5JcNF1in0lWBdxZ5AMOWFr62N1bR1DxApL9o9MJTWItq6qopofTYRKWz9gyHqiizn3j7TGkYcU1qIsU3ogKu6opwLj9lViLmUamlv6Rygua6y4BJn1lZ6QUSpBQPOOXoHQykvfJ3aUM3AUDg6DdjWHYiOBqaivro8Wi5ERApb31Ao+iG5WDQW2ZqzQjChAy6Ab52zqxDzXS9syWNLMqu9O5DyeqJsigQk/SU2whUIhgmFXcoLXyPTqwd/9V5WtXXT3h1gakPqP7/Wxmo2d2qXokgx6BssvoBrtOUqJTRmkRUTPuACmOrv6lq2qXRKzmzrCTClvvACrsn+vH97d2CcK4tLJI1DqtOA4Zg3qjf+8GF6AkEaa1L/BLlweiNrd/Qp27xIgQuFHYPBMHWVxTdi9H9+WTJJjAIu4A8ffi0AT67ZkeeWZM7Kth5a6gsrJQTAjKYazGBTTAqEUvD9e18GSHlK8XX7DC943Z/mJ97ZLXU4B1u7SqufRUpNv/+hqLaq+P4c7z6LUmB7tApO8f2Es2Dv1gb2bq2nqcByVqUqEAzR2T+UUg6nbKsoL2NyXVXJjXDd/KSX82ogGE7p8bvXuxwMhalJMS0E7MrFta2ntPpZpNRE1rPWFknh6lgzm4e/b2lKcWyF9xc5TwaGwty3bCtDodT+YBaSTj+x6KJ5k/PcktFt7x3k5ifXlcwmhdjvIxxO/Xtac9WZw+7f+NialJ9rTov3RrhuR1/KzyEi2RdZz1psuxQBpk8qrNJxhU4Bly9SVuWah17Jc0vSF6lVWGhZ5ndXKqkh1mzfFdScd9SctJ7rlkteE7399iNmp/w8kTfCti6NcIkUsl1TisUXcAH87MLDo7eV+HRsCrh83/DLFPzwvpfz3JL0dQ14AdekAg24jt3bW68UaWexe3ptBwCHzWlOOw3HrJgh+ncuSj14iyze/9PTG9Jqj4hkV+SDZ7EGXKcflHy914kqrYDLzJrN7E9m9pKZLTez15rZZDO7z8xW+v+3+Neamf3UzFaZ2XNmdkRmvoXMeFcJ5eOK1CqclMYut2w6d5E3cvNqe2+eW5IZn/njUu//U/ZN+7n28Mtl1FSWsXD6yMSCiYps2Q6lMcUpItlXzFOKAOVlu1bKD6a4hnWiSHeE6yfA3c65/YBDgeXA5cADzrkFwAP+fYDTgQX+v0uAa9L82hllZtFSKsU+yhWZHi3U+fVVbT0AfPCmxXluSWYdPT/9NXOV5WWsuepMXrry9LTLMr35kBmEi/zDg0ipK/YRLoD/ftNCACrKNGk2lpR7x8yagBOA6wCcc4POuZ3AWcCN/mU3Amf7t88CbnKex4FmMyuoscjrLjoKgP/956o8tyQ9W7sGKC8z9ijQgOuS4/cG4OT9p+e5JelzzjG1oYpzDp9FVRq1D7NhRlMNW7oGin7EVqSURdZwpZpSphBc+oZ9mNpQTXVlYb0HFpp0emc+0A7cYGbPmNmvzKwemO6c2+xfswWI/FWdBayPefwG/9gwZnaJmS02s8Xt7e1pNC95xy2YGr1dzH+ktnYFmNpQRVlZYSZFaarz1pb9femmPLckfdc+vJptPYNp1T3MlpnNtQwMhdnRO5jvpohIHP1FnBYioqzM2GdaPf9eua1k1uZmQzoBVwVwBHCNc+5woJdd04cAOC9qSSpycc5d65xb5Jxb1NramkbzUlPl566658XiLfPzp6c3sLVIdqcVe03Fb9/1EgDbewuvvyML8Dd0qMSPSKGKTikW6RquiMdXe4nDv33n8jy3pHClE3BtADY4557w7/8JLwDbGpkq9P9v889vBGK3Xc32jxWUP37Eyzr/kd8uKepRrmIpLLqtu7hHX472c519+22H5LklI81uqQPg3mXF++FBpNRFAq5inlIE2H/GJABufnJ9ydXKzZSUAy7n3BZgvZlFtmadDCwDbgcu8o9dBNzm374deK+/W/E1QGfM1GPBOHROc/T2/cvb4l9YoCI1/T72hn3y3JKxXf++RQCs3taT55akZ0vXAG89dGZBVimY5Sc/vfrB4s8tJ1KqBoZCmBHdtFWsfvfBY6K3H16Z2+VAxSLdn/AngN+Z2XPAYcC3gKuAN5nZSuCN/n2AO4HVwCrgl8DH0vzaWbPfHo0AfKgId9G1+SVzdq9xVWhaG7wF/S8WccFw5xxbOgeY0VSYmxMKMQgUkeH6BkPUVZanvSs53ybXV0UHLD78m6fz25gClVbA5Zx71l9vdYhz7mznXIdzbrtz7mTn3ALn3Budczv8a51z7lLn3N7OuYOdcwUbzfzswl0pwoJFVuonUqNw2qTCDrj2n+EFtcWct2VH7yCDoXA0d1Yh+uyp3gD0wJCG+EUKUV+aheoLyadOLuyZlXwr7jHMLNlnmlfMGmCfL9yV59Ykp617AIDWAh/hqigvwwxWtRfvlOLmTq+vdy88XUgiiU9Xbi3efhYpZV0DQ0yqKY3R6BMXToveXq86riMo4IrjLx97XfR2MS2ej9TOm9ZYuKMuEc7BHc9tLqr+jfXPl7w1fpPrq/LckvjmTfU+OCzfUrxTtyKlrLNviOa60gi4ysqMMw/20mse/90Hi34XeqYp4Iojdv3L9+5ZUTQlUtp7AlSWGy1F9Av8uT8/l+8mpCQSJx4wc1J+GzKGN+7vfeKMTDWLSGHZ0TtIS13hfmhL1k8v2FXM+oJfPjHGlROPAq4x3PnJ4wH4+UOv8O5fFccLp60rQGtDdVEswDzeTzR76+LiLLB862Ivj299Aa+/qPOTKX7vnhV5bomIjGZbT6Dgl4Ako7zM+OvHjgVg6fqddPYVRiLUFVu6eXFTZ17boIBrDLEjF4+t3h6tAVjI1u/oK+hF3LFufP/R+W5CWgJBbyF6MQS3AE+v3ZHvJohIjFDYsb13sKQCLoBDZzdHb5//y8fz15AYP3ngZT51y7N5bYMCrnG88LVTo7ff+MN/5bElidnWGyjoRdyxYksPrS7CxfNT6qt5w765r4aQqhseWZPvJohIjI6+QUJhV3IBV1mZ8Rd/lGv55q6CGOXa1jOY9/W2CrjG0VBdwRWn7xe9//HfL2FDR+HuvthZZAsw33aEV06zGAuGb+rsZ2Zz4Qe3r377DICCrPcoMpFF1la2NpRWwAVwxNyW6O1Dv35vdAlGvmzrDjBFAVfh+/Dr9+amD3jTX/94bjPHfefBglxEHw47dvYV1wLML555AAB/fabgqjyNqW8wSPdAMJrNvZCZGYfNaeaWp/L7hiciw0UDrhIb4YqIrdzyP3/K3+ao/sEQq7f1MjvP79cKuBJ0wsLhU0d7f/5O5l1+B+u2F85oV/dAkLCjqEa4YndTzrv8jqJJ0PnSlm6Ags0yv7up/ifo3z+xLs8tEZGIUg+4fnvx8HW6j72yPS/t+OyflgLw6rb8/r1WwJWEV799xogCoyd870EefKkwai529HmFoItphMvM+MMlr4ne3+9Ld3PHcwVXYnOEbf4b5d6tDXluSWIuP93LOP/5vz6f55aISMSWLi95cjHkTUxFY00la646M3r/gl8+ztUPrmLp+p05bcc//L8pXzhz/5x+3d0p4EqCmbHs66fxt0tfN+z4+3/9FPMuv4PfPL6Wddv78rZAcGe/93Vb6otnhAvgmL2mDLt/6e+XcPcLW/LUmsT0+gn9GoskQ/Q+0xqjty+4tjB2DYkUuqFQmFDY0TUwxIubOtneE2De5Xdw5/PJfygMxyxDGRgKMe/yO/jePSuYVFNRMqV94vnu2w+J3v7ePSs46+pH+NfLuStwHamPPN9PBJ0vWkWbgsPmNLPmqjP5wl+f53cxUzRf+tsL0dv3X3bCsD9yuRBZlFhbWXw/1jVXncm8y++I3v/Ib73ip19764FcdOy8PLUqvp4BL+AqpoXoXzhjf75553IeW72dgaEQNZWl/SYvkojtPQH6BkOUlxk9gSB1VeW0NlZzwJfvibtW92O/WzLs/qkHTufk/adz7pGzMTP+9XI7H/nN0+w5pY53HDmbb9yxPO7X7xoo/Wzs7zxqDlMaqrj4xl0llC+6/kkAnvvqKVkvbVRTWR7N+5hPVshlVRYtWuQWLy7YGtcEgiFuXbyBXz68mnVx6kY11Vby5BdOproie3/cOnoHaamv4tN/eJa/PrMxJy/gbAiHHQu/eBfBOG9yPzn/MA6b08zcyXV5z331nbtf4pcPr2bZ10+jqqJ4Bopjg9pvv+1g9ppaP2KEsRA559jSNUBvIEgw7Hh01XZmNtdy/X9e5ck1w/OLXfOuIzh5/+lUVZTx4Io2jp43mfoiCowl+waDYe5dtoVt3QG++vdl+W7OsGm3Ura5s5+l63fykd8uGfX8I5efRGNNBbWV5VSWZ+Z9tTcQZNE37ufk/afxswuPyMhzjsXMnnbOLRr1nAKuzHhhYyer2nr4rz88O+r5cw6fxffPPZTysuQChVDYsb0nwOT6KgZDYYaCjmA4zMad/Xz19hdZsm7niMcU8y9vOOz4xM3P8Mgr29g5xtTs8q+fNmwYPhx2w/J6ZVskcCm2vv7475dE1zNE3Prh17L/jMa8T4865zAzgqEwa7b30tYV4NkNO/nu3ZnJkn/mITO49MR9mD6pmua6qqR/F6WwDYXCvLqtl3+taOe3T6ylzIxj5k/mlfYeTj1wj+gMwMtJFnJ/37Hz2NDRz/3Lt0bv/33pJrb3DqbV3s+cspAPHr/XhBxpfmZdB+f8/NExr5k3pY7bLj2Ojr5BGmsqmBKTOmPFlm7++VIbl5ywF2Hn2PeLd3HwrCaO2LNlzHyDuXi/VsCVY/2DIfb/8t1xz7/v2HkcPreZ1+0zlV8+vJpLT9qHJWs7uOGRNbz10JkATJ9Uw4qt3Vz5j+Q/fRVbEBDP6vYeTvrB+MlmP/z6vfi/f62mosy46eKjqa4opycQ5IQFU7MyEuacY/4Vd1JVXsbL3zw948+fTY+u2saFccpU/eLdR9JSV8n0STXRote7u+IvzxMYCvHD8w6LHgsEQ1SVl0X72jlHIBiO/iEJhsLcu2wrf1y8nrMOm0VdVTlPr+ugvTvAp9+4kK1dA7zjF49l9htNwKdOXsCeU+roHQxxzuGzWLGlm1faejhhYSvdA0MsmO4tCdjc2U9LXVVCfxgDwRBL13eycWcf27oH2X/GJJr9Pm1trGYwGGZL5wCzWmpHBHyRgDPWjt5BysuMptpKdvTuSty4dnsvobBjryLZtJFtf1y8nq/9fRk9gcxOz63+1hljfpBzznHTY2tZv6OPX/3n1RHn95xSx1HzJvPdtx+S0w+ExaBrYIhDvnpvzr7eaQfuwS/ec2TWv44CrjwIhsJ8447l/PrRNTn9uj8671DOOXx2Tr9mtoXDjr0+f2fKj3/k8pP489MbcA6OXziVxuoKysuMSbWVTG2oJhx2LNvcRfdAkNfsNRkzo617gJ/cv5LD57awrSfAG/efxrRJNXzst0t4pb2HzZ0DfOUtB/D+183P4HeaGwNDIVZu7eEtP/tPys/xoePnc9J+07kgx2U7zj9qTjSf2K/eu4juwBDnHD6bwWCY5Zu7CIbD/Pbxdfxn1bacFew+eb9pPJDiTuX99miMphhJ1YJpDayMKTt2+8dfx68fXcMhs5roHgjyjkWz6R4I0hsI8pclG3nzITOYP7WeB15q47xFc7j4xqd4/cJWDprVxD+e28y5i2azYks3l926lI+euDdv2HcaM5trmNpQzW8eW8uZh8yguqKMptpKKjI07ZOMt/38EfbdYxIfPH4+JyfwgWwsv//gMfzx6Q18+20HZ3SkaWAoRHt3gDmT6zL2nKVoYChEZXkZD77UxmFzmznuO/9kYCicla/10pWn5WQ0UQFXnjnn+NQtz3L70k0Zeb69Wut5x5GzOXHhNPaf0Zj39Uy5EAo7Nnf2M6u5lm/csZzrRvk0mWs3feDoEfnZis0fF6/ns3lMSDiWynLj9Qtb+fwZ+7Olc4Aj9mxJ6Q3zlfYe7n1xK9t6AgXxuillx+0zlfOPnsO8KfV85LdP85ePHZtSyoObHlvDQbOaotnKV2zp5tQfP5zw46++8Ai2dg3w9X8so6LMCDvH5afvx4Ezm5g7uY6G6gpa8px1XMa2tWuAB5a3JZzK5sYPHM1HfvM05x89h9fsNYW27gDvec2eWW7lSAq4CshLW7o47cf/5u1HzGbt9l4OndM84o/AHz/yWpprK9m7tYH7l2/lmPlTaCqiZKa5EgyFueP5zdy/vI2fnHcYO/uH2NE7mLOaly9+7dSSWYy9qq2bZZu7+cwflzIYzM4nzAXTGrjm3Udw27ObOHR2M4+8so1bn1rPV996IMfuM5VpjdUZWyg7lr7BIL9/Yh0bOvpprKng1W29VJQZ+82YxDPrOnh6bQdfPPOAuOsxx1NmEHbeaPO67f386P6Xh50/b9Ec/pDnMif5dtS8Fp5a0+F9cNy3lXtf3Jr2B9IygxvefzTlZhxXADvSZGJSwFXgOvuG6BkM8r8PrOSK0/dXcJUm5xyhsKOivIyO3kE++6fnuOrtB/O1vy/j70m8qd/w/qP40t9eYENH/4hzV551IO957bwMtrowbe7sZ3vPIE+t2cE5h8+iua6KgaEQbV0BpjRUjQg4N3f20zMQZNqkGlZu7WbRvMl5annmDYXCPPbKdo5fMJXFazuoqypnY0c/j76ynctP32/c0bcl6zooN4uWO3HOcfvSTcydXMfhc1vY3hOgua6KNdt7mT+lnp7BIIvX7ODIuZPpGhiKTk/1DQbpGwwxtaGazv4hAsEQHb1DTJ9UTTDs2NYTYHZLHXc8t4nysjIu//NznLtoNut29HHYnGaufvCVuG0898jZ/PHpDQCcftAezG6p5Zf/LsxRwd998BjmTa2nNxBk4fTcpuARiSerAZeZlQOLgY3OuTeb2XzgFmAK8DTwHufcoJlVAzcBRwLbgfOcc2vGeu6JEnBJbvX6uXYCwTBtXQHmTtm1zmJVWzflZWV5T5Ankm3OOZas6+CIuS3DliX0D4YYCoejqWXCYUf3QJCGmgr6BoNUV5SzdntvdDRyZnMtD7/czsq2Hmoqy/jVv1/lh+88lI/+bgk70tzJF+vH5x3G3Cl1VJWXcdCspow9r0gmZTvgugxYBEzyA65bgb84524xs18AS51z15jZx4BDnHMfMbPzgXOcc+eN9dwKuEREipNzjmDYsXnnACd870G++/ZDOHafKTTXVXmjoH79wLBzDIbCXHT9kxw9fzIzm2t51zG5X3sjkglZC7jMbDZwI/BN4DLgLUA7sIdzLmhmrwW+6pw71czu8W8/ZmYVwBag1Y3RAAVcIiIiUizGCrjSXaH6Y+B/gMgq2ynATudcJBnKBmCWf3sWsB7AP9/pX797Yy8xs8Vmtri9PXe1lkRERESyJeWAy8zeDLQ5557OYHtwzl3rnFvknFvU2lrcW+5FREREIL3i1a8D3mpmZwA1wCTgJ0CzmVX4o1izgY3+9RuBOcAGf0qxCW/xvIiIiEhJS3mEyzl3hXNutnNuHnA+8E/n3LuAB4F3+JddBNzm377dv49//p9jrd8SERERKRXZyDL4OeAyM1uFt0brOv/4dcAU//hlwOVZ+NoiIiIiBScjabKdcw8BD/m3VwNHj3LNAHBuJr6eiIiISDHJfeVRERERkQlGAZeIiIhIlingEhEREcmygi5ebWbtwNp8tyMNU4Ft+W7EBKG+zg31c26on3ND/ZwbE6mf93TOjZpEtKADrmJnZovjpfiXzFJf54b6OTfUz7mhfs4N9bNHU4oiIiIiWaaAS0RERCTLFHBl17X5bsAEor7ODfVzbqifc0P9nBvqZ7SGS0RERCTrNMIlIiIikmUKuERERESyTAGXiIxgZpbvNohkgl7LUigUcKXJzE4ys/p8t6OUmefDZjYj320pdWb2TTPb32lxZ9aZ2Swzq/JvKyjInsrIDfVz9phZU6R/1c+jU8CVIjN7l5k9DbwBGMp3e0qVmZ0KvAQcC1TluTkly8wuNLOHgY8B7853e0qZmZ1nZi8APwJ+A6AAN/PM7AL/PfqbZvYpUD9ng5m93czWAj8FfgLq53gq8t2AYuJH7RXAp4AvAKc75x7Pb6tKl5lVAGcAn3TO3bPbOdMvdXrMrAxoBL4LzAOuAPYHmvzz6uMMM7Oj8N4/LnHOPWpmy83sCOfckny3rZSY2SLgE8ClwCrgATPrds5dr9d15phZK/Bh4DxgKfBvM/sY8H/OuVBeG1eANMKVIDOrcZ4h4GXgd8BaM6vyI/yZeW5iSTCzmsht51wQ2BdY7w9X/7eZvUlvmOkzs1rnXNg51wlc65w71Tn3COCAd4I+pWZK7GsamA884gdb04EXgJ15aViJ2a2f9wcecM497pzbhvd+/S0za9LrOqPCQB+w0znXj/dh4q3AYflsVKFSwJUAM/sScLeZfdLMFgJ3Aev9/5cA5wA3mtkX/OvVrymI6eePm9kh/uFVwFHAX4FW4PPAj82sMU/NLHp+P9/lv54Pds49HfOa/TMQjOl/ScNu7x17As8Be5rZH4GnAAN+ZWbf8a/X2pcU7NbPc4AVwOlmdoB/SRjoAj7tX6/36BSY2dfM7MyYQ3XAdqDF/yD8CLAMb8RL/bwbdcY4zOwDwMnA5/Aqnn/P//924F7gNOfcu/F+kT9jZlOcc+F8tbdY7dbP04ArzWwy8CrwXuAO59zleOuLXgvsla+2FrNRXs9XmtmeMa/ZFrw+13tDmkbp65/ijQS8E1gJfNE59w7gYuC9ZjZLoy/JG6WffwYsB/4C/I+/jmsacCHwZjOr13t0csxsspldC3wSb6SwEsA5tx7YAbwZmOJf/iPgnWY2Tf08nN5Ux+B/2pwD/Nw59wTeWpcXge8455YDX3bObQBwzr0A3I33Cy9JiNPPy4GrgP8FgkC1mdU55zbiTenOz1d7i1Wcfn4Br58BcM69CuyJPyWgT6ipGaOvf+RfUo83EhDp80eBhXloalEb473jx865b+FNcV3snPsfYBtePw9qJDFpvcDfnHMtwEbgsphzPwcOBo7zl96sB/4NaFf5bvRm6hvtFzDm0+Z7/fs9eG+Y+5vZic65Af+xlWb2v8AkYG2OmlyUkujnHwBHAvvhjSq2Al80sx/6x7TIeAxJ9PNPgL3N7MSYS/8IvMm/Rp9Qx5FEX/8YWGBmBwJtwJfM7BQz+z4wCy8gkziSfO84xMxOcs51OueeNS/9xpeAkHNuSCOJ8cXp5wDwsH/3K8CHzE/T439g+D1wOvADM/s53oeHNTlpcBFRwLVL9EVmPv/uVcBeZnaCf38b3lbuU/xrz8b71BQCzo0EYRJXMv38W+Dtzrn7ge8AHUAn8Hrn3LoctrkYJdvPp8Q8dgD4q0YBEpZMX/8OOBvv9fxP4CP+uZOdc+25aW7RSvY9+mT/2iPw+hq8nbgytmH9HLntnOvx12k9BfwLuDLmMX8AvgpswVvTdbK/IUdiTPi0EGZ2Bt621lfM7Hbn3EPOOWdm5f6LK2BmV+ONshzjnwvhzVuDlyPqHc45jWyNIcV+HgS6AZxzW8zs+/pkOrY0Xs/bY57mBn83rowhjdd0pb8D9ydm9n/6kDa2NF7THf5TrMX74LY1T99CURijn8sgOtpdjrfE43K8FBAL8NZumXPuMTP7ht6j45uQI1z+h6MqM/sBXlT+C7yt2ReY2dEAzrmQcy5oZjOcc1cDvWZ2lZkdh7ftNfIifEnB1ugy1M+xn7D0izyKTL6e/WsVbMWRob6Ovo4VbI0uk+8dzrntCrZGl2A/h51zYfOmEKv8Y+3APXi7Qa/BC8L0Hj2OCRlwOc8g3k6hC51zdwHXAc14U4OYWYWZfRf4s5nNAz6INyf9TeBh59x389D0opKhfv5eHppeVNTPuaO+zg31c24k2c9/AA70g7Q34wW1lzvnDvenGWUcE2pK0cw+ibeb4knn3C+Ba/3jVc65jebldorsMtwbr3/OdM5FhqZ/YWbX+y9QiUP9nBvq59xRX+eG+jk3UuznsyL9bGYrgMOc1mklZcKMcJnZ+/DysPwZeLeZXQHM94dLB82sBajGK0+Ac26Fc+4y51yHmZVHnke/yGNTP+eG+jl31Ne5oX7OjTT7ucI/tlLBVvImTMCFt2PlO865u4H/xntBvSvm/Dyg03mLs2ebWWSHiznVhEqG+jk31M+5o77ODfVzbqTTz8Gct7aElHzAZbsSNz6Dlw0X59xi4HFglpkd75+fBZSb2SeAO4A9/Gu1CDAB6ufcUD/njvo6N9TPuaF+zr+SC7jMbA///9itrACPAGW2K1fLC8Am/BcTXqLHtwD7AGc4536Xs0YXIfVzbqifc0d9nRvq59xQPxeekgm4zOxwM3sAPxlb5MUVE9WvxCvLc56ZlTuvJM8eeAsCwZvPfpNz7lPOKx8jo1A/54b6OXfU17mhfs4N9XPhKvqAyzw/Am4CbnTOfSjmXFlMVN+NV9+pGvi+ecU3W/BKbOCce9g590BuW1881M+5oX7OHfV1bqifc0P9XPiKPuDy55UbgWecczcBmNnesS8wM7sSr9ZTJ149rRa8F1wncGNeGl5k1M+5oX7OHfV1bqifc0P9XPisGNfBmdlrgB3OuZf9+5OAp/DqlJ0KbAUixWJ78DLoftk5t8q/vgyod85157zxRUT9nBvq59xRX+eG+jk31M/FpahGuMys2czuAO4D3mlm9QDOuS7gauDteMVJLwA2A+cCHc65C51zq2IXD+oFFp/6OTfUz7mjvs4N9XNuqJ+LU1GNcJnZLLwXUhewL175hrtizre4XZlwXwt8HTjbOde72xy2jEH9nBvq59xRX+eG+jk31M/FqeBHuMzsvWb2ejOb5LwdE9cCtwIDwDFmNjNyrdtV3gHgSGA9fj0ovcDGpn7ODfVz7qivc0P9nBvq5+JXkAGXv9tihpk9CFyElwX3GjOb6pwbcM71AffjLfg7KeZx1WZ2opktxpu/vso5N5CP76EYqJ9zQ/2cO+rr3FA/54b6ubQUXMBlXl6QyG6Ljc65k4GPAjvwC2wCOOcewasMv5+ZNZlZrXMuAAwC33DOvSWykFBGUj/nhvo5d9TXuaF+zg31c+kpmDVc5hUfvRIoB+4EJgHvcM5d5J8vw8uGe55z7l/+sQbgG8DrgLnA4c65TXloftFQP+eG+jl31Ne5oX7ODfVz6SqIES4zez3wNN6w6Cq8F9sQ8AYzOxqi885f9f9FnAl8DHgWOFgvsLGpn3ND/Zw76uvcUD/nhvq5tFXkuwG+MPAD59xvwCtNAMwHvgxcAxzpR/V/A04ys3nOuTV4iwXf6Jx7OC+tLj7q59xQP+eO+jo31M+5oX4uYQUxwoUX0d/qD6WCV1xzrnPu1/hVy/2ofjYQ8l9gOOdu0wssKern3FA/5476OjfUz7mhfi5hBRFwOef6nHMB51zIP/QmoN2//X5gfzP7B3AzsAS83Ru5b2lxUz/nhvo5d9TXuaF+zg31c2krlClFILpY0AHTgdv9w93A54GDgFedX73cFcpq/yKkfs4N9XPuqK9zQ/2cG+rn0lQQI1wxwkAlsA04xI/kvwSEnXP/ibzAJG3q59xQP+eO+jo31M+5oX4uQQWTFiLCvGKcj/r/bnDOXZfnJpUk9XNuqJ9zR32dG+rn3FA/l55CDLhmA+8Bfui85G2SBern3FA/5476OjfUz7mhfi49BRdwiYiIiJSaQlvDJSIiIlJyFHCJiIiIZJkCLhEREZEsU8AlIiIikmUKuERERESyTAGXiJQsM/uqmX1mjPNnm9kBuWyTiExMCrhEZCI7G1DAJSJZpzxcIlJSzOwLwEVAG7AeeBroBC4BqoBVeAklDwP+4Z/rBN7uP8XVQCvQB3zIOfdSDpsvIiVKAZeIlAwzOxL4NXAMUAEsAX6BVxplu3/NN4Ctzrn/NbNfA/9wzv3JP/cA8BHn3EozOwb4tnPupNx/JyJSairy3QARkQw6Hvirc64PwMxu948f5AdazUADcM/uDzSzBuBY4I9mFjlcne0Gi8jEoIBLRCaCXwNnO+eWmtn7gBNHuaYM2OmcOyx3zRKRiUKL5kWklDwMnG1mtWbWCLzFP94IbDazSuBdMdd3++dwznUBr5rZuQDmOTR3TReRUqaAS0RKhnNuCfAHYClwF/CUf+pLwBPAI0DsIvhbgM+a2TNmtjdeMHaxmS0FXgTOylXbRaS0adG8iIiISJZphEtEREQkyxRwiYiIiGSZAi4RERGRLFPAJSIiIpJlCrhEREREskwBl4iIiEiWKeASERERybL/B89n1OSTD62NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot variables\n",
    "\n",
    "for var in df.columns[0:-1]:\n",
    "    df[var].plot(figsize=(10, 5))\n",
    "    plt.title(var)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52471e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>co2</th>\n",
       "      <th>humidity_ratio</th>\n",
       "      <th>id</th>\n",
       "      <th>occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-02-04 18:00:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.175000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.00</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-04 18:01:00</td>\n",
       "      <td>23.075</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>690.25</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-02-04 18:02:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>691.00</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-04 18:03:00</td>\n",
       "      <td>23.100</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>419.0</td>\n",
       "      <td>683.50</td>\n",
       "      <td>0.004751</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-02-04 18:04:00</td>\n",
       "      <td>23.050</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>687.50</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  temperature   humidity  light     co2  humidity_ratio  \\\n",
       "0 2015-02-04 18:00:00       23.075  27.175000  419.0  688.00        0.004745   \n",
       "1 2015-02-04 18:01:00       23.075  27.150000  419.0  690.25        0.004741   \n",
       "2 2015-02-04 18:02:00       23.100  27.100000  419.0  691.00        0.004739   \n",
       "3 2015-02-04 18:03:00       23.100  27.166667  419.0  683.50        0.004751   \n",
       "4 2015-02-04 18:04:00       23.050  27.150000  419.0  687.50        0.004734   \n",
       "\n",
       "   id  occupancy  \n",
       "0   1          0  \n",
       "1   1          0  \n",
       "2   1          0  \n",
       "3   1          0  \n",
       "4   1          0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace target\n",
    "\n",
    "df = (\n",
    "    df.reset_index(drop=False)\n",
    "    .drop(\"occupancy\", axis=1)\n",
    "    .merge(occ.reset_index(drop=False), on=\"id\")\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6aa2a70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# verify target\n",
    "\n",
    "df[\"occupancy\"].plot(figsize=(15, 5))\n",
    "plt.title(\"occupancy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af30d37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
